Side channel information analysis based on machine learning

Ehsan Saeedi, Yinan Kong

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

10 Citations (Scopus)

Abstract

Cryptographic devices, even after recent improvements, are still vulnerable to side channel attacks(SCA). The majority of the available literature of SCA belongs to the traditional methods such as simple and differential analysis methods and template attacks, whilst few studies based on machine learning are available. In this paper, we investigate the side channel analysis based on machine learning techniques in the form of principal component analysis (PCA) and support vector machine (SVM). For this purpose, we verify the efficiency of RBF and POLY kernel functions of SVM classifier under the influence of the number of principal components (PCs). Our experimental results, obtained by cross validation method, comprise the accuracy and computational complexity of this method and can show the validity and the effectiveness of the proposed approach.

Original languageEnglish
Title of host publication2014, 8th International Conference on Signal Processing and Communication Systems, ICSPCS 2014 - Proceedings
EditorsTadeusz A. Wysocki, Beata J. Wysocki
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1-7
Number of pages7
ISBN (Electronic)9781479952557
DOIs
Publication statusPublished - 23 Jan 2014
Event8th International Conference on Signal Processing and Communication Systems, ICSPCS 2014 - Gold Coast, Australia
Duration: 15 Dec 201417 Dec 2014

Other

Other8th International Conference on Signal Processing and Communication Systems, ICSPCS 2014
CountryAustralia
CityGold Coast
Period15/12/1417/12/14

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    Saeedi, E., & Kong, Y. (2014). Side channel information analysis based on machine learning. In T. A. Wysocki, & B. J. Wysocki (Eds.), 2014, 8th International Conference on Signal Processing and Communication Systems, ICSPCS 2014 - Proceedings (pp. 1-7). [7021075] Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ICSPCS.2014.7021075